Isn't this all simply about correlation vs causation? Machine learning can find strong correlations and we can make predictions based on those correlations, but

Isn't this all simply about correlation vs causation? Machine learning can find strong correlations and we can make predictions based on those correlations, but at the end of the day, the machine knows nothing about what is causing any of it, and hence is "inscrutable". So it is up to us to fill the gap in our understanding because that is what machine learning ultimately says about the subject. It tells us what we don't know. If we knew all about the subject, our predictions would match the predictions of the machine because there is only one reality we're both observing. But if there is any gap, then the machine is telling us what we don't know, not what it (of all things) knows. It's just crunching numbers. It doesn't "know" anything.